CN102098421B - Automatic smoothing method of GAMMA data of liquid crystal display television (LCDTV) - Google Patents

Abstract

The invention relates to television technologies and provides an automatic smoothing method of GAMMA data of a liquid crystal display television (LCDTV). The smoothing method is used for solving the problem that smoothing is performed only based on the position relations among adjacent points in the existing smoothing method of GAMMA data. The technical scheme is as follows: a set of GAMMA data are set as original GAMMA data, and whole GAMMA table data are divided into n intervals; intervals with large amplitude of variation are determined, intervals with continuously large amplitude of variation form a zone with large amplitude of variation, and a starting point and an end point are determined; a straight line is fitted in the zone with the large amplitude of variation to obtain fitted data, smoothing coefficients are determined, and linear data are determined; the linear data, the smoothing coefficients and the original GAMMA data are calculated to obtain final GAMMA data; and the automatic smoothing is carried out on the LCDTV. The smoothing method has the beneficial effects of reducing influence on integral brightness curves and being suitable for the LCDTV.

Description

LCD TV GAMMA data automatic smoothing method

Technical field

The present invention relates to TV tech, particularly the level and smooth technology of the GAMMA data of LCD TV.

Background technology

Only carry out level and smooth in the smoothing method of existing GAMMA data according to the relation of the position between the consecutive points; Shortcoming is not consider the attribute of GAMMA data in integral body; When LCD TV uses the GAMMA table that colour temperature and brightness curve are adjusted; Because it is bigger that the colour temperature consistency of part liquid crystal display screen appears at colour temperature adjustment GAMMA table data variation amplitude later than missionary society; This situation can be amplified this variation in the adjustment of follow-up brightness curve, make that the whole brightness curve performance continuity of liquid crystal display screen is bad, thereby the whole image quality of LCD TV is exerted an influence; Therefore when using GAMMA curve adjustment liquid crystal display screen colour temperature method; Must accomplish to take into account to the influence of overall brightness curve in consistency that keeps colour temperature and minimizing, promptly in the trend that keeps the GAMMA curve, reduce its amplitude of variation, general whole GAMMA table data all have 2 ^y* 256 data, wherein y is the integer more than or equal to 0.

Summary of the invention

The objective of the invention is to overcome in the smoothing method of present GAMMA data and only carry out level and smooth shortcoming, a kind of LCD TV GAMMA data automatic smoothing method is provided according to the relation of the position between the consecutive points.

The present invention solves its technical problem, and the technical scheme of employing is that LCD TV GAMMA data automatic smoothing method is characterized in that, may further comprise the steps:

A. set one group of GAMMA data as original GAMMA data, and with m data serve as at interval with whole GAMMA table data be divided into n interval and number in order, define whole GAMMA and show to have R data in the data, n=R/m then, wherein, R=2 ^y* 256, y is the integer more than or equal to 0, m=2 ^z, z is the integer between 3 to 8;

B. confirm according to the relation of GAMMA data in each interval whether this interval is the interval of vary within wide limits;

C. will occur interval that amplitude of variation reaches continuously based on the common zone of forming a vary within wide limits of algorithm, confirm the starting point and the terminal point in the zone of this vary within wide limits;

D. in the zone of vary within wide limits, all GAMMA data use least square methods are carried out fitting a straight line, obtain data after the match;

E. confirm the interior smoothing factor in zone of this vary within wide limits according to the relation between data after the match and the original GAMMA data;

F. utilize the starting point and the terminal point in the zone of this vary within wide limits to confirm that straight line is as linear data;

G. utilize linear data, smoothing factor and original GAMMA data computation to obtain final GAMMA data;

H. according to these final GAMMA data LCD TV GAMMA data are carried out automatic smoothing.

Concrete, step b may further comprise the steps:

B1. a given threshold values is designated as T ₁

B2. calculate all n interval amplitude of variation parameters in the whole GAMMA table data, its computing formula is:

$D_j={\left(\underset{x=1}{\overset{m}{\mathrm{\Σ}}}{|G(j\×m+x)-G(j\×m+x+1)|}^2\right)}^{\frac{1}{2}}/m$

Wherein, D _jRepresent j interval amplitude of variation parameter, x representes j the data in the interval, and (j * m+x) the whole GAMMA of expression shows the j * m+x data in the data to G;

B3. judge D _jWhether greater than T ₁If should the interval be the interval of vary within wide limits then, if not should the interval be the interval of non-vary within wide limits then.

Further, step c may further comprise the steps:

C1. select the interval of a minimum vary within wide limits of numbering based on the numbering in the interval of each vary within wide limits of judging;

C2. write down the numbering k in the interval of this vary within wide limits of selecting, the starting point that defines the zone of this vary within wide limits is k * m;

C3. judge whether occurring the interval of two non-varys within wide limits continuously through behind d interval again,, continue to judge if not then get back to the c3 step if then get into next step;

C4. the terminal point that defines the zone of this vary within wide limits is (k+d+1) * m, confirms the zone of this vary within wide limits;

C5. judge whether that the big interval of all changes amplitude has all put under in the zone of each vary within wide limits; If then get into the d step; If not then from the numbering in the big interval of all changes amplitude, extract each interval numbering in the zone that does not put vary within wide limits under; Select the interval of a minimum vary within wide limits of numbering, get into the c2 step.

Concrete, step e may further comprise the steps:

E1. a given threshold values is designated as T ₁

E2. calculate the diversity factor between the data and original GAMMA data after the match, its computing formula is:

$\mathrm{\ϵ}=\left(\underset{i=s}{\overset{p}{\mathrm{\Σ}}}{|\mathrm{Ln}\left(i\right)-G\left(i\right)|}^2\right)/(p-s)$

Wherein, ε is the diversity factor between data and the original GAMMA data after the match; S is this regional starting point, and p is this regional terminal point, and i representes in this zone from certain point data between origin-to-destination; Ln (i) is the data after this area data match in the whole GAMMA table data, G (i) be GAMMA show in the data should the zone from the data between the origin-to-destination;

E3. judge that whether the ε value that calculates is greater than T ₂If then its smoothing factor is designated as α, α is defined as α ₁, if not then its smoothing factor is designated as α, α is defined as α ₂, α ₁And α ₂Be set point.

Further again, step g may further comprise the steps:

G1. utilize linear data, smoothing factor and original GAMMA data computation to obtain final GAMMA data, its computing formula is:

G _o(i)＝αG(i)+(1-α)L(i)

Wherein, i representes in this zone from certain point data between origin-to-destination, G ₀(i) be should the zone in the final GAMMA table data from the data between the origin-to-destination; α is a smoothing factor; L (i) be should the zone in the resultant GAMMA of the step f table data linear data, G (i) be GAMMA show in the data should the zone from the data between the origin-to-destination.

The invention has the beneficial effects as follows; Through above-mentioned LCD TV GAMMA data automatic smoothing method, can be when using GAMMA curve adjustment liquid crystal display screen colour temperature, the consistency while that as far as possible keeps colour temperature; Minimizing is to the influence of overall brightness curve; Improved the quality and the performance of product, helped to improve competition capability, promotion is further magnified.

Embodiment

Below in conjunction with embodiment, describe technical scheme of the present invention in detail.

LCD TV GAMMA data automatic smoothing method of the present invention is: at first set one group of GAMMA data as original GAMMA data; And serve as at interval whole GAMMA table data to be divided into n interval and to number in order with m data; Define in the whole GAMMA table data and have R data; N=R/m then, wherein, R=2 ^y* 256, y is the integer more than or equal to 0, m=2 ^zZ is the integer between 3 to 8; Confirm according to the relation of GAMMA data in each interval whether this interval is the interval of vary within wide limits again; And interval that amplitude of variation reaches will appear continuously according to the common zone of forming a vary within wide limits of algorithm; Confirm the starting point and the terminal point in the zone of this vary within wide limits, in the zone of vary within wide limits, all GAMMA data use least square methods are carried out fitting a straight line, obtain data after the match; Confirm the interior smoothing factor in zone of this vary within wide limits again according to the relation between data after the match and the original GAMMA data; And utilize the starting point and the terminal point in the zone of this vary within wide limits to confirm straight line as linear data, and utilize linear data, smoothing factor and original GAMMA data computation to obtain final GAMMA data then, final system carries out automatic smoothing according to these final GAMMA data to LCD TV GAMMA data.

Embodiment

The LCD TV GAMMA data automatic smoothing method that this is routine can the consistency while that as far as possible keeps colour temperature, reduce the influence to the overall brightness curve when using GAMMA curve adjustment liquid crystal display screen colour temperature.

Its method is: at first set one group of GAMMA data as original GAMMA data; And with m data serve as at interval with whole GAMMA table data be divided into n interval and number in order, define whole GAMMA and show to have R data, then n=R/m in the data; Wherein, R=2 ^y* 256, y is the integer more than or equal to 0, m=2 ^zZ is the integer between 3 to 8; Confirm according to the relation of GAMMA data in each interval whether this interval is the interval of vary within wide limits again; And interval that amplitude of variation reaches will appear continuously according to the common zone of forming a vary within wide limits of algorithm; Confirm the starting point and the terminal point in the zone of this vary within wide limits, in the zone of vary within wide limits, all GAMMA data use least square methods are carried out fitting a straight line, obtain data after the match; Confirm the interior smoothing factor in zone of this vary within wide limits again according to the relation between data after the match and the original GAMMA data; And utilize the starting point and the terminal point in the zone of this vary within wide limits to confirm straight line as linear data, and utilize linear data, smoothing factor and original GAMMA data computation to obtain final GAMMA data then, final system carries out automatic smoothing according to these final GAMMA data to LCD TV GAMMA data.

Wherein, confirm that according to the relation of GAMMA data in each interval whether this interval is that the method in the interval of vary within wide limits is: an at first given threshold values is designated as T ₁, calculate all n interval amplitude of variation parameters in the whole GAMMA table data again, its computing formula is:

$D_j={\left(\underset{x=1}{\overset{m}{\mathrm{\Σ}}}{|G(j\×m+x)-G(j\×m+x+1)|}^2\right)}^{\frac{1}{2}}/m$

Wherein, D _jRepresent j interval amplitude of variation parameter, x representes j the data in the interval, and (j * m+x) the whole GAMMA of expression shows the j * m+x data in the data to G, judges the D that calculates then _jWhether greater than T ₁If should the interval be the interval of vary within wide limits then, if not should the interval be the interval of non-vary within wide limits then; With occurring interval that amplitude of variation reaches continuously according to the common zone of forming a vary within wide limits of algorithm; Starting point and the method for terminal point of confirming the zone of this vary within wide limits are: the interval of at first selecting a minimum vary within wide limits of numbering according to the numbering in the interval of each vary within wide limits of judging; Write down the numbering k in the interval of this vary within wide limits of selecting then; The starting point that defines the zone of this vary within wide limits is k * m; Judging after again through d interval whether occur the interval of two non-varys within wide limits continuously again, if not then continue to judge, is (k+d+1) * m if then define the terminal point in the zone of this vary within wide limits; Confirm the zone of this vary within wide limits; Judge whether that at last the big interval of all changes amplitude has all put under in the zone of each vary within wide limits, if then get into next step, if not then from the numbering in the big interval of all changes amplitude, extract each interval numbering in the zone that does not put vary within wide limits under; Select the interval of a minimum vary within wide limits of numbering, get back to that step of starting point in the zone of confirming this vary within wide limits; The method of confirming the regional interior smoothing factor of this vary within wide limits according to the relation between data after the match and the original GAMMA data is: an at first given threshold values is designated as T ₁, calculate the diversity factor between the data and original GAMMA data after the match then, its computing formula is:

$\mathrm{\ϵ}=\left(\underset{i=s}{\overset{p}{\mathrm{\Σ}}}{|\mathrm{Ln}\left(i\right)-G\left(i\right)|}^2\right)/(p-s)$

Wherein, ε is the diversity factor between data and the original GAMMA data after the match, and s is this regional starting point, and p is this regional terminal point; I representes in this zone from certain point data between origin-to-destination; Ln (i) is the data after this area data match in the whole GAMMA table data, G (i) be should the zone in GAMMA table data from the data between the origin-to-destination, whether the ε value that judgement at last calculates greater than T ₂If then its smoothing factor is designated as α, α is defined as α ₁, if not then its smoothing factor is designated as α, α is defined as α ₂, α ₁And α ₂Be set point; The method of utilizing linear data, smoothing factor and original GAMMA data computation to obtain final GAMMA data is: utilize linear data, smoothing factor and original GAMMA data computation to obtain final GAMMA data, its computing formula is:

G _o(i)＝αG(i)+(1-α)L(i)

Wherein, i representes in this zone from certain point data between origin-to-destination, G ₀(i) be should the zone in the final GAMMA table data from the data between the origin-to-destination; α is a smoothing factor; L (i) be should the zone in the resultant GAMMA of the step f table data linear data, G (i) be GAMMA show in the data should the zone from the data between the origin-to-destination.

Claims (1)

1. LCD TV GAMMA data automatic smoothing method is characterized in that, may further comprise the steps:

A. set one group of GAMMA data as original GAMMA data, and with m data serve as at interval with whole GAMMA table data be divided into n interval and number in order, define whole GAMMA and show to have R data in the data, n=R/m then, wherein, R=2 ^y* 256, y is the integer more than or equal to 0, m=2 ^z, z is the integer between 3 to 8;

B. confirm based on the relation of GAMMA data in each interval whether this interval is the interval of vary within wide limits, and it specifically may further comprise the steps:

B1. a given threshold values is designated as T ₁

B2. calculate all n interval amplitude of variation parameters in the whole GAMMA table data, its computing formula is:

$D_j={\left(\underset{x=1}{\overset{m}{\mathrm{\Σ}}}{|G(j\×m+x)-G(j\×m+x+1)|}^2\right)}^{\frac{1}{2}}/m$

Wherein, D _jRepresent j interval amplitude of variation parameter, x representes j the data in the interval, and (j * m+x) the whole GAMMA of expression shows the j * m+x data in the data to G;

B3. judge D _jWhether greater than T ₁If should the interval be the interval of vary within wide limits then, if not should the interval be the interval of non-vary within wide limits then;

The starting point and the terminal point in the zone of this vary within wide limits confirmed based on the common zone of forming a vary within wide limits of algorithm in the interval that c. will occur vary within wide limits continuously, and it specifically may further comprise the steps:

C1. select the interval of a minimum vary within wide limits of numbering based on the numbering in the interval of each vary within wide limits of judging;

C2. write down the numbering k in the interval of this vary within wide limits of selecting, the starting point that defines the zone of this vary within wide limits is k * m;

C3. judge whether occurring the interval of two non-varys within wide limits continuously through behind d interval again,, continue to judge if not then get back to the c3 step if then get into next step;

C4. the terminal point that defines the zone of this vary within wide limits is (k+d+1) * m, confirms the zone of this vary within wide limits;

C5. judge whether that the big interval of all changes amplitude has all put under in the zone of each vary within wide limits; If then get into the d step; If not then from the numbering in the big interval of all changes amplitude, extract each interval numbering in the zone that does not put vary within wide limits under; Select the interval of a minimum vary within wide limits of numbering, get into the c2 step;

D. in the zone of vary within wide limits, all GAMMA data use least square methods are carried out fitting a straight line, obtain data after the match;

E. confirm the interior smoothing factor in zone of this vary within wide limits according to the relation between data after the match and the original GAMMA data, it specifically may further comprise the steps:

E1. a given threshold values is designated as T ₂

E2. calculate the diversity factor between the data and original GAMMA data after the match, its computing formula is:

$\mathrm{\ϵ}=\left(\underset{i=s}{\overset{p}{\mathrm{\Σ}}}{|\mathrm{Ln}\left(i\right)-G\left(i\right)|}^2\right)/(p-s)$

Wherein, ε is the diversity factor between data and the original GAMMA data after the match; S is this regional starting point, and p is this regional terminal point, and i representes in this zone from certain point data between origin-to-destination; Ln (i) is the data after this area data match in the whole GAMMA table data, G (i) be GAMMA show in the data should the zone from the data between the origin-to-destination;

E3. judge that whether the ε value that calculates is greater than T ₂If then its smoothing factor is designated as α, α is defined as α ₁, if not then its smoothing factor is designated as α, α is defined as α ₂, α ₁And α ₂Be set point;

F. utilize the starting point and the terminal point in the zone of this vary within wide limits to confirm that straight line is as linear data;

G. utilize linear data, smoothing factor and original GAMMA data computation to obtain final GAMMA data, it specifically may further comprise the steps:

G1. utilize linear data, smoothing factor and original GAMMA data computation to obtain final GAMMA data, its computing formula is:

G _o(i)＝αG(i)+(1-α)L(i)

Wherein, i representes in this zone from certain point data between origin-to-destination, G ₀(i) be should the zone in the final GAMMA table data from the data between the origin-to-destination; α is a smoothing factor; L (i) be should the zone in the resultant GAMMA of the step f table data linear data, G (i) be GAMMA show in the data should the zone from the data between the origin-to-destination;

H. according to these final GAMMA data LCD TV GAMMA data are carried out automatic smoothing.